Neural Network Threat Detection Systems for Data Breach Protection

This study presents a comprehensive investigation into the development of an effective neural network-based threat detection system aimed at identifying and preventing data leaks. Employing a diverse array of Machine Learning methods, including K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and various Gradient Boosting models, this research paper delves into the optimization of cybersecurity measures through advanced analytical and modeling techniques. Central to our approach is the deployment of a deep multilayer perceptron neural network, designed to accurately detect a wide range of cyber threats that could potentially lead to data breaches. The research methodology encompasses analysis and synthesis, systematization, classification, and a detailed comparative analysis to evaluate the efficacy of each model. The outcome of this study is a sophisticated software solution capable of not only detecting complex threat patterns but also enabling the export of results in CSV format for further examination. This paper contributes to the cybersecurity field by showcasing the potential of neural networks and Machine Learning in creating robust, efficient threat detection systems, thereby enhancing data protection strategies against the backdrop of evolving cyber threats.